Machine learning models for predicting post-cystectomy recurrence and survival in bladder cancer patients

Abstract

Currently in patients with bladder cancer, various clinical evaluations (imaging, operative findings at transurethral resection and radical cystectomy, pathology) are collectively used to determine disease status and prognosis, and recommend neoadjuvant, definitive and adjuvant treatments. We analyze the predictive power of these measurements in forecasting two key long-term outcomes following radical cystectomy, i.e., cancer recurrence and survival. Information theory and machine learning algorithms are employed to create predictive models using a large prospective, continuously collected, temporally resolved, primary bladder cancer dataset comprised of 3503 patients (1971-2016). Patient recurrence and survival one, three, and five years after cystectomy can be predicted with greater than 70% sensitivity and specificity. Such predictions may inform patient monitoring schedules and post-cystectomy treatments. The machine learning models provide a benchmark for predicting oncologic outcomes in patients undergoing radical cystectomy and highlight opportunities for improving care using optimal preoperative and operative data collection.

3D Non-Local Neural Network: A Non-Invasive Biomarker for Immunotherapy Treatment Outcome Prediction. Case-Study: Metastatic Urothelial Carcinoma

“…The feasibility of predicting the response to immunotherapy treatment for patients with neoplastic diseases in the metastatic phase has been recently investigated using standard machine learning and deep learning methods. Traditional machine learning methods based on the analysis of high-dimensional clinical data and CT-based diagnostic imaging have been proposed in order to predict the outcome of bladder cancer treatments [ 24 , 25 , 26 ]. Specifically, Reference [ 24 ] reports a comparative analysis of different machine learning methods used to process high-dimensional clinical data with the aim to predict mortality after a radical cystectomy in a large dataset of bladder cancer patients.…”

Development and Validation of a Machine Learning Prognostic Model for Hepatocellular Carcinoma Recurrence After Surgical Resection

“…In patients with bladder cancer, a novel predictive model based on machine learning algorithms was also created. In the model, disease recurrence after cystectomy was predicted with more than 70% sensitivity and specificity ( 13 ). However, few studies have applied a machine learning framework to identify HCC patients with the potential risk of recurrence after curative treatment.…”

Bladder cancer in the time of machine learning: Intelligent tools for diagnosis and management

“…Hasnain et al 23 used a combination of various machine learning methods to build a predictive model with clinicopathological features from a large prospective, primary bladder cancer dataset of 3503 BC patients. Patient recurrence and survival 1, 3, and 5 years after RC were predicted with greater than 70% sensitivity and specificity.…”